A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

Han, Bin; Zhang, Hui; Sun, Ming; Wu, Fengtong

doi:10.3390/s21227762

Cited by 36 publications

(25 citation statements)

References 33 publications

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“…The work conducted by Walambe et al [ 35 ] used a multimodal framework for stress detection and achieved a promising accuracy of 96.09% for the SWELL dataset in 2021. Han et al [ 36 ] successfully used the application of GAF images in their experiment to introduce a new Bearing Fault Diagnosis Method, which is not related to stress recognition, but showed a promising approach to using GAF images and their implementation which certainly helped us to understand more about time series images in this regard. The authors of [ 37 ] successfully introduced a hierarchical deep neural network for mental stress state detection using IoT-based biomarkers.…”

Section: Resultsmentioning

confidence: 99%

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

et al. 2022

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The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experiment has been conducted on two standard benchmark datasets, namely WESAD (wearable stress and affect detection) and SWELL. During the studies, testing accuracies of 94.8% and 99.39% are achieved for the WESAD and SWELL datasets, respectively. For the WESAD dataset, chest data are taken for the experiment, including the data of sensor modalities such as three-axis acceleration (ACC), electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), etc.

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Section: Resultsmentioning

confidence: 99%

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

et al. 2022

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“…Thereinto, GAF, MTF and RP are widely used as common coding methods. For example, in order to obtain more complete fault characteristics, Han et al [28] encoded the original vibration sensor signal into a two-dimensional image through GAF and MTF and input the GAF image and MTF image into the capsule network as a dual channel image. They also discussed the impact of two coding methods and different network structures on diagnostic accuracy.…”

Section: B Methods Based On Two-dimensional Featuresmentioning

confidence: 99%

Time Series Classification Based on Multi-Dimensional Feature Fusion

et al. 2023

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Time series classification is a key problem in data mining, most of existing classification methods directly extract one-dimensional data from one-dimensional features, which cannot effectively express the inter-relation between different time points. Besides, some classification methods extract two-dimensional features through encoding raw one-dimensional data into two-dimensional images, and part of information is lost due to the difference of encoding methods. How to make full use of one-dimensional and two-dimensional features to extract valuable information and integrate them in an optimal fashion remains a promising challenge. In this paper, we propose a multi-scale convolutional network to extract one-dimensional features from time series for obtaining more feature information based on multi-scale convolution kernels. Two-dimensional features are constructed in terms of two-dimensional image coding based on Gramian angular field, M arkov transition field and Recurrence plot (GMR) methods. We develop a multi-dimensional feature fusion approach leveraging Squeeze-and-Excitation (SE) and Self-Attention (SA) mechanism to effective fusing one-dimensional multi-scale features and two-dimensional image features in terms of weight setting. We conduct experimental verification based on 84 complete data traces from a typical UCR dataset in the field. Experimental results show that the accuracy of our proposed approach improves by 3.35% compared with existing benchmark methods. The Gradient-weighted Class Activation M apping (Grad-CAM ) visualization analysis method is adopted, where our proposed approach extracts more accurate features and effectively distinguishes different time series data categories.

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“…To complete fault classification, Chen et al transformed the signal data into a time-frequency map and combined CNN with a square pooling structure and an extreme learning machine (ELM) [23]. Han et al presented a method for converting the signal for Gramian angular field (GAF) image coding into an image combined with a capsule network for diagnosing bearing faults [24]. Inspired by the previous work and combined with the requirement of fast and accurate fault diagnosis with minimal human influence, this study presents a novel method which is the first to combine the GAF-CNN and ELM aspects.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Diagnosing Motor Bearing Faults Based on Gramian Angular Field Image Coding and Improved CNN-ELM

et al. 2023

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The most common cause of electric motor failure is the bearings, and so methods for fast and accurate diagnosis of motor bearing failure are urgently needed. Traditional fault diagnosis methods have high uncertainty and complexity since they require manual extraction of features. Deep learning has shown good performance in electrical equipment fault detection, and it can directly complete end-to-end diagnosis of motor faults, avoiding human involvement. Here, a new fault diagnosis method is presented which combines Gramian angular field (GAF) image coding, extreme learning machine (ELM) and convolutional neural network (CNN). The method has three main stages: First of all, GAF is utilized to convert the acquired vibration break signals into 2-D pictures. Next, the enhanced CNN model is taken to identify the elements of the converted image quickly and accurately. Finally, the ELM is used as the final classifier to gain further accuracy and diagnostic speed of fault classification. Experiments were designed to validate the proposed method using two different motor bearing fault datasets at Case Western Reserve University and autonomous experiment and performance is compared with several commonly used intelligent diagnosis algorithms. The proposed method's accuracy in the experiment designed in this paper can reach 99.2% at most, and it only takes 0.835s to complete the diagnosis, which outperforms traditional diagnostic methods on both datasets and improving the maximum diagnostic accuracy by 33.6%. The findings indicate that this method can classify various fault types efficaciously, and has the benefits of quick diagnosis, high accuracy, and good generalization ability.INDEX TERMS Gramian angular field image coding, convolutional neural network, extreme learning machine, fault diagnosis.

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A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

Cited by 36 publications

References 33 publications

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

Time Series Classification Based on Multi-Dimensional Feature Fusion

A New Method for Diagnosing Motor Bearing Faults Based on Gramian Angular Field Image Coding and Improved CNN-ELM

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